Meeting Invitee Recommendation Using Relationship Scores
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Solution Overview
Problem
Existing meeting invitee recommendation systems primarily rely on past meeting attendees' roles, which may not reflect changes in roles or meeting types, leading to suboptimal invitations for new meetings.
Innovation Solution
A computer-implemented method that uses electronically stored relationship data to select additional invitees based on their roles, historic meeting data, and relationship scores, generating a meeting profile to recommend individuals with matching roles and opportunities for future meetings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If recommendation systems use past meeting attendees' roles, then the invitation process is simplified and automated, but the accuracy of invitee recommendations decreases when roles change or meeting types differ
Solution Approach 1:
The system dynamically adapts the recommendation approach based on meeting similarity. For similar meetings, it uses automated role-based recommendations from past attendees. For dissimilar meetings, it transitions to a more manual process requiring organizer input, thus maintaining automation where effective while ensuring accuracy when conditions change.
Solution Approach 2:
The system changes the recommendation parameters based on meeting characteristics. When meetings are similar, it relies on historical role data. When meetings differ significantly, it adjusts by incorporating organizer preferences and specific meeting context, thereby maintaining both automation and accuracy across different scenarios.
2Device complexity
If the system considers only identity of past attendees, then the recommendation process is simpler, but it fails to account for role changes and different meeting types
Solution Approach 1:
The system segments the recommendation process into two distinct paths: one for similar meetings using automated role-based suggestions, and another for dissimilar meetings requiring manual organizer input. This segmentation allows the system to maintain simplicity where applicable while gaining adaptability when needed.
Solution Approach 2:
The system dynamically selects between different recommendation approaches based on meeting similarity assessment. This dynamic behavior enables the system to remain simple for routine similar meetings while becoming adaptable and complex only when necessary for dissimilar meetings.
3Measurement precision
If the system uses comprehensive relationship data and role information, then the accuracy of invitee recommendations improves, but the data processing complexity increases
Solution Approach 1:
The system performs preliminary actions by pre-processing and storing relationship data and meeting histories in structured formats before they are needed for recommendations. This preliminary organization reduces the complexity of data processing during actual recommendation generation, as the data is already prepared and accessible.
Solution Approach 2:
The system creates simplified copies or representations of complex relationship data and meeting histories that can be efficiently processed during recommendation generation. By working with these streamlined representations rather than the full complexity of raw data, the system achieves high accuracy without proportionally increasing processing complexity.
Data Source
AI summary
Computer implemented method and a system that includes receiving a list of invitees for a future meeting, accessing electronically stored relationship data that includes information identifying a plurality of individuals and existing relationships between the individuals, wherein the individuals include at least some of the invitees and also additional individuals, selecting one or more of the additional individuals that are identified in the relationship data as having existing relationships with one or more of the invitees, and adding the one or more selected additional individuals to a potential invitee list for the future meeting.